Wildlife conservation posts
Explore the latest in our mission to build a better world using data science and AI.
Content list
What's a takahē?
Although Zamba's models are trained with animals from Africa and Europe, they can be used with videos from other locations that show species the models have never seen. We demonstrate with a dataset from New Zealand.
Standing on the Threshold
Using probabilistic classifications from Zamba, we can automatically remove a large majority of blank videos while controlling the fraction of wildlife videos we lose. But how do we know where to draw the line?
Find all the pangolins
We can use Zamba's probablistic classifications to search for videos containing specific animals. Particularly for small animals, this strategy can be highly effective.
You can stop watching blank videos
Using Zamba's probablistic classifications, you can identify and remove blank videos -- saving viewing time, storage space, and data transfer costs -- while minimizing the loss of videos that contain animals.
Computer Vision for Wildlife Monitoring in a Changing Climate
Through the Patrick J. McGovern Foundation Accelerator, DrivenData and the Wild Chimpanzee Foundation are teaming up to create automated, accurate, and accessible species detection tools.
Meet the winners of the Where's Whale-do Challenge
Meet the winners of the Where's Whale-do challenge, and learn about the deep learning models they developed to identify individual Cook Inlet beluga whales from images.
Where's Whale-Do? Data Exploration and Benchmark
How to get started with the Where's Whale-do beluga photo-identification challenge!
Introduction to Image Classification using Camera Trap Images
We'll show you how to get started identifying animal species from camera trap images!
Meet the winners of the Deep Chimpact: Depth Estimation for Wildlife Conservation Challenge
Meet the minds behind the top models for measuring wildlife depth! Accurate depth estimations help ecologists track wildlife populations and protect the ecosystems that depend on them.
Deep Chimpact: Depth Estimation for Wildlife Conservation - Benchmark
In this guest post by MathWorks, we'll show you how to start working with camera trap videos to estimate the distance to an animal seen in the wild.
Meet the winners of the Hakuna Ma-Data Challenge
Meet the winners who built the best wildlife identifiers!
How to Use Deep Learning to Identify Wildlife
We show you how to get off to a wild start on our animal identification competition using a neural network!
Results from Pri-Matrix Factorization and a New Open Source Tool for Wildlife Research and Conservation
Using AI to study the natural world: check out the results!
Pri-matrix Factorization - Benchmark
How about some deep learning to identify wild animals in camera traps? Here's a benchmark post to get contributors started in our newest challenge.
Random walk of the penguins
The goal of this data science competition was to reach out to the data science community to build a model that predicts penguin populations.
Random Walk of the Penguins - Benchmark
Penguins are so adorable! Here's our first pass at predicting penguin populations in Antarctica.
Building the best naïve bees classifier!
These are the people who were best able to distinguish honeybees from bumblebees and how they did it.
Bumble bee or honey bee?
We're excited to launch a new comeptition with our partner Metis. The question at hand is: can you identify a bee as a honey bee or a bumble bee?
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